Positioning sensor and direction estimation method

ABSTRACT

A positioning sensor includes a transmission antenna transmitting a transmission signal, a plurality of reception antennae, each receiving a reception signals, a receiver observing the each of the plurality of reception signals in a predetermined period, a processor, and a memory, in which the processor calculates a plurality of complex transfer functions based on the each of the plurality of reception signals, records each of the plurality of complex transfer functions in the memory as being associated with each time point, extracts, among the plurality of complex transfer functions, a plurality of pairs of two complex transfer functions respectively corresponding to two time points in a predetermined interval, calculates a plurality of pieces of differential information, and estimates to a location of a moving body based on each of the plurality of pieces of differential information.

BACKGROUND

1. Technical Field

The present disclosure relates to a positioning sensor and an estimationmethod for estimating a direction and a location of a moving body byusing a wireless signal.

2. Description of the Related Art

A method using a wireless signal has been examined as a method forrecognizing a location and the like of a person (for example, seeJapanese Unexamined Patent Application Publication (Translation of PCTApplication) No. 2014-512526, International Publication No. 2014/141519,and Japanese Unexamined Patent Application Publication No. 2015-117972).Japanese Unexamined Patent Application Publication (Translation of PCTApplication) No. 2014-512526 discloses a method for detecting a livingbody by using a Doppler sensor and International Publication No.2014/141519 discloses a method for detecting a motion of a person andliving body information by using a Doppler sensor and a filter. JapaneseUnexamined Patent Application Publication No. 2015-117972 discloses thata location and a state of a person who is a detecting object can berecognized by analyzing a component containing a Doppler shift with theFourier transformation.

SUMMARY

However, there is such problem that presence/absence of a person can bedetected by the methods of the related art but a direction and alocation on which a person exists cannot be detected.

Further, there is another problem that it is difficult to detect adirection in which a living body such as a person exists and a locationon which a living body exists with high accuracy in a short period oftime. This is because frequency change derived from biological activitybased on the Doppler effect is extremely small and observation needs tobe performed in a long period of time (for example, several tens ofseconds) in a stationary state of a living body so as to observe thefrequency change by the Fourier transformation. Further, this is becausea living body generally does not keep the same posture or location forseveral tens of seconds.

In one general aspect, the techniques disclosed here feature apositioning sensor that includes a transmission antenna that transmits atransmission signal to a predetermined area in search of a moving body;a plurality of reception antennae, each of which receives a receptionsignal, one or more of a plurality of the reception signals receivedincludes a reflection signal generated by the moving body reflecting thetransmission signal; a receiver that observes each of the plurality ofreception signals in a predetermined sampling cycle in a predeterminedperiod; a processor; and a memory, wherein the processor calculates aplurality of complex transfer functions, each of the plurality ofcomplex transfer functions representing a propagation characteristicsbetween the transmission antenna and each of the plurality of receptionantennae based on each of the plurality of reception signals, recordseach of the plurality of complex transfer functions in the memory asbeing associated with each time point at which each of the plurality ofreception signals is observed, each of the plurality of receptionsignals corresponding to each of the plurality of complex transferfunctions, extracts, among the plurality of complex transfer functions,a plurality of pairs of two complex transfer functions respectivelycorresponding to two time points in a predetermined interval, calculatesa plurality of pieces of differential information representing adifference between a pair of two complex transfer functions included ineach of the plurality of pairs of two complex transfer functions, eachof the plurality of pieces of differential information being expressedby a vector of N dimensions, and estimates a direction to a location ofthe moving body with respect to the positioning sensor based on each ofthe plurality of pieces of differential information.

These general and specific aspects may be implemented using a system, amethod, and a computer program, and any combination of systems, methods,and computer programs.

According to the present disclosure, a direction and the like in which amoving body exists can be highly accurately estimated in a short periodof time by using a wireless signal.

Additional benefits and advantages of the disclosed embodiments willbecome apparent from the specification and drawings. The benefits and/oradvantages may be individually obtained by the various embodiments andfeatures of the specification and drawings, which need not all beprovided in order to obtain one or more of such benefits and/oradvantages.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an example of the configurationof an estimation device according to a first embodiment;

FIG. 2 illustrates an example of a detection object of the estimationdevice illustrated in FIG. 1;

FIG. 3 conceptually illustrates a state of transfer of signal waves inan antenna unit illustrated in FIG. 1;

FIG. 4 is a conceptual diagram illustrating an example of two timepoints in a predetermined interval used in calculation of differentialinformation in the first embodiment;

FIG. 5 is a conceptual diagram illustrating an example of two timepoints in a predetermined interval different from that in FIG. 4;

FIG. 6 is a flowchart illustrating estimation processing of theestimation device according to the first embodiment;

FIG. 7 is a block diagram illustrating an example of the configurationof an estimation device according to a second embodiment;

FIG. 8 illustrates an example of a detection object of the estimationdevice illustrated in FIG. 7;

FIG. 9 is a flowchart illustrating estimation processing of theestimation device according to the second embodiment;

FIG. 10 illustrates a concept of an experiment using the estimationmethod according to the second embodiment;

FIG. 11 illustrates a result of an experiment using the estimationmethod according to the second embodiment; and

FIG. 12 illustrates a result of another experiment using the estimationmethod according to the second embodiment.

DETAILED DESCRIPTION (Underlying Knowledge Forming Basis of the PresentDisclosure)

A method using a wireless signal has been examined as a method forrecognizing a location and the like of a person.

For example, Japanese Unexamined Patent Application Publication(Translation of PCT Application) No. 2014-512526 discloses a method fordetecting a living body by using a Doppler sensor and InternationalPublication No. 2014/141519 discloses a method for detecting a motion ofa person and living body information by using a Doppler sensor and afilter.

Further, for example, Japanese Unexamined Patent Application PublicationNo. 2015-117972 discloses that a wireless signal is transmitted to apredetermined region and the wireless signal reflected at a detectionobject is received by a plurality of antennas so as to estimate acomplex transfer function between transmission/reception antennas. Thecomplex transfer function is a function of complex numbers representinga relation between an input and an output, and represents a propagationcharacteristics between the transmission/reception antennas in thisexample. The number of elements of this complex transfer function isequal to a product obtained by multiplying the number of transmissionantennas by the number of reception antennas.

Japanese Unexamined Patent Application Publication No. 2015-117972further discloses that a location and a state of a person who is adetecting object can be recognized by analyzing a component containing aDoppler shift with the Fourier transformation. More specifically,temporal change of a component of a complex transfer function isrecorded so as to perform the Fourier transformation with respect to atemporal waveform of the temporal change. Living activity such asbreathing and heartbeat of a living body such as a person slightlyprovides the Doppler effect to a reflection wave. Accordingly, acomponent containing a Doppler shift includes an influence of a person.On the other hand, a component having no Doppler shift is not affectedby a person, that is, corresponds to a reflection wave from a fixedobject or a direct wave between transmission/reception antennas. Thus,Japanese Unexamined Patent Application Publication No. 2015-117972discloses that a location and a state of a person who is a detectingobject can be recognized by analyzing a component containing a Dopplershift.

In a similar manner, the Fourier transformation is performed withrespect to an observed signal so as to take out a Doppler componentderived from a person (living body) in Japanese Unexamined PatentApplication Publication No. 2015-072173, Japanese Unexamined PatentApplication Publication No. 2015-119770, Japanese Patent Application No.2013-558810, Japanese Unexamined Patent Application Publication No.2014-215200, Japanese Unexamined Patent Application Publication No.2015-117961, and International Publication No. 2012/115220, for example.Further, it is disclosed that the Doppler component which is taken outis analyzed so as to detect a location of the living body and states ofheartbeat, breathing, and the like of the living body.

Further, F. Adib, Z. Kabelac, D. Katabi, and R. Miller, “3D tracking viabody radio reflections”, 11th USENIX Symp. Net. Systems Design & Impl.(USENIX NSDI '14), April 2014, for example, discloses a method fordetecting a direction and a location of a human body without using theFourier transformation. In this document, propagation response in anunmanned state is preliminarily measured and a differential component isanalyzed on the assumption that difference between the unmanned stateand a manned state is caused by a person, so as to estimate a locationof the person. More specifically, in a location estimation methoddisclosed in this document, frequency response in a wide band of 1 GHzor larger is observed and propagation time of an extracted reflectionwave derived from a person is calculated so as to estimate distancesfrom a plurality of antennas located on different positions and estimatea location of the person by using the estimated distances. In thisdocument, temporal response of a complex propagation channel in a mannedstate is observed and subtraction between complex propagation channelsof different time is performed so as to extract only a reflection wavewhich is derived from a person and from which a reflection componentfrom fixed objects such as a wall and a store fixture is eliminated.

Further, for example, Dai Sasakawa, Keita Konno, Naoki Honma, KentaroNishimori, Nobuyasu Takemura, Tsutomu Mitsui, “Fast Estimation Algorithmfor Living Body Radar”, 2014 International Symposium on Antennas andPropagation (ISAP 2014), FR3D, pp. 583-584, December 2014 and JapanesePatent Application No. 2013-558810 disclose a method for estimating adirection of a living body by eliminating unnecessary components from acomplex transfer function obtained in a manned state. More specifically,a complex transfer function in an unmanned state is preliminarilymeasured so as to eliminate a reflection wave from a fixed object and adirect wave between transmission/reception antennas from the complextransfer function. Then, since a complex transfer function in a mannedstate includes a reflection wave from a fixed object and a direct wavebetween transmission/reception antennas, the complex transfer functionin the unmanned state is subtracted from the complex transfer functionin the manned state so as to eliminate unnecessary components.

However, by the above-mentioned method of Japanese Unexamined PatentApplication Publication (Translation of PCT Application) No. 2014-512526and International Publication No. 2014/141519, presence/absence of aperson can be detected, but a direction and a location on which a personexists cannot be detected.

Further, in the above-mentioned method of Japanese Unexamined PatentApplication Publication No. 2015-117972, observation time of severaltens of seconds is required so as to perform the Fourier transformation.Therefore, it is difficult to highly accurately detect a direction and alocation of a person in a short period of time. This is becausefrequency change derived from biological activity based on the Dopplereffect is extremely small and observation needs to be performed in along period of time (for example, several tens of seconds) in astationary state of a living body so as to observe the frequency changeby the Fourier transformation. A living body generally does not keep thesame posture or location for several tens of seconds. Therefore, ifobservation time is shortened, a signal derived from a living bodycannot be properly extracted by the Fourier transformation, degradingaccuracy in estimation of a direction and a location of a person.

This problem, that is, the above-mentioned problem of JapaneseUnexamined Patent Application Publication No. 2015-117972 can occur in asimilar manner also in the disclosures of Japanese Unexamined PatentApplication Publication No. 2015-072173, Japanese Unexamined PatentApplication Publication No. 2015-119770, Japanese Patent Application No.2013-558810, Japanese Unexamined Patent Application Publication No.2014-215200, Japanese Unexamined Patent Application Publication No.2015-117961, and International Publication No. 2012/115220.

Further, there is a problem in which a complex transfer function at anunmanned state needs to be preliminarily measured in the method ofJapanese Patent Application No. 2013-558810, “3D tracking via body radioreflections”, and “Fast Estimation Algorithm for Living Body Radar”.This is because a location of a person cannot be estimated in a case ofan occurrence of change of a propagation environment itself such astransfer of store fixtures such as furniture. Since it is conceivablethat chairs, tables, and the like are frequently moved given theapplication to an environment in which a person lives, it is difficultto apply the above-mentioned method of Japanese Patent Application No.2013-558810, “3D tracking via body radio reflections”, and “FastEstimation Algorithm for Living Body Radar” to a living environment of aperson.

Thus, there is a problem in which a direction and the like in which amoving body exists cannot be highly accurately estimated in a shortperiod of time by using a wireless signal, in the related art.

Further, in recent years, a radar has been studied which estimates anexisting direction and the like of a living body in a radio wavepropagation environment in which a multiple wave exists, by using afeature in which a living body creates Doppler shift on a radio wave bycertain living activity such as breathing and heartbeat. That is, aradar has been studied which irradiates a living body with a radio wave,thereby eliminates signal components which do not go through the livingbody by the Fourier transformation of a reception signal, and estimatesan incoming direction of a radio wave reflected from the living body soas to estimate a direction of the living body.

However, a direction of a living body cannot be highly accuratelyestimated in a short period of time by using the Fourier transformation,as described above.

The inventors have conceived an estimation device and the like by whicha direction and the like in which a moving body exists can be highlyaccurately estimated in a short period of time by using a wirelesssignal.

(1) A positioning sensor according to an aspect of the presentdisclosure includes a transmission antenna that transmits a transmissionsignal to a predetermined area in search of a moving body; a pluralityof reception antennae, each of which receives a reception signal, one ormore of a plurality of the reception signals received includes areflection signal generated by the moving body reflecting thetransmission signal; a receiver that observes each of the plurality ofreception signals in a predetermined sampling cycle in a predeterminedperiod; a processor; and a memory, wherein the processor calculates aplurality of complex transfer functions, each of the plurality ofcomplex transfer functions representing a propagation characteristicsbetween the transmission antenna and each of the plurality of receptionantennae based on each of the plurality of reception signals, recordseach of the plurality of complex transfer functions in the memory asbeing associated with each time point at which each of the plurality ofreception signals is observed, each of the plurality of receptionsignals corresponding to each of the plurality of complex transferfunctions, extracts, among the plurality of complex transfer functions,a plurality of pairs of two complex transfer functions respectivelycorresponding to two time points in a predetermined interval, calculatesa plurality of pieces of differential information representing adifference between a pair of two complex transfer functions included ineach of the plurality of pairs of two complex transfer functions, eachof the plurality of pieces of differential information being expressedby a vector of N dimensions, and estimates a direction to a location ofthe moving body with respect to the positioning sensor based on each ofthe plurality of pieces of differential information.

With this configuration, a direction in which a moving body exists canbe estimated with high accuracy in a short period of observation timecorresponding to a cycle derived from activity of the moving body.Accordingly, a direction in which a moving body exists can be highlyaccurately estimated in a short period of time by using a wirelesssignal.

(2) In the aspect, among a plurality of pairs of two time points, eachpair of two time points in a predetermined interval may include a firsttime and a second time, the first time being a point in time that may beearlier than the second time, and the first time may vary for each ofthe plurality of pairs of two complex transfer functions.

Accordingly, an influence of instantaneous noise can be reduced bytaking an average of two or more pieces of differential information, sothat accuracy in direction estimation can be further improved.

(3) In the aspect, the moving body may be a living body.

(4) In the aspect, the predetermined period may be approximately a halfcycle of at least one of a breathing cycle, a heartbeat, and a bodymovement of the living body.

Accordingly, a direction in which a living body exists can be estimatedthrough observation in a period corresponding to a cycle of at least oneof breathing, heartbeat, and body movement.

(5) In the aspect, among a plurality of pairs of two time points, eachpair of two time points in a predetermined interval may include a firsttime and a second time, the first time being a point in time that isearlier time than the second time, and the processor for each of theplurality of pairs of two time points, may calculate a correlationmatrix with respect to a differential time between the second time andthe first time based on each of the plurality of pieces of differentialinformation, may apply a predetermined method to each of the correlationmatrices to estimate an incoming direction of the reflection signal withrespect to the positioning sensor, and may estimate a direction to alocation of the moving body with respect to the positioning sensor basedon the incoming direction.

(6) In the aspect, the predetermined method may be a multiple signalclassification (MUSIC) algorithm.

(7) A positioning sensor according to another aspect of the presentdisclosure includes M transmission antennae, M being a natural number of2 or larger, each transmission antenna transmits a transmission signalto a predetermined area in search of a moving body; N receptionantennae, N being a natural number of 2 or larger, each receptionantenna receives a reception signal, one or more of the receptionsignals received includes a reflection signal generated by the movingbody reflecting the transmission signal; a receiver that observes eachof the reception signals received in a predetermined sampling cycle in apredetermined period; a processor; and a memory, wherein the processorcalculates M×N pieces of complex transfer functions, each of the M×Npieces of complex transfer functions representing a propagationcharacteristics between each of the M transmission antennae and each ofthe N reception antennae based on each of the reception signalsreceived, records each of the M×N pieces of complex transfer functionsin the memory as being associated with each time point at which each ofM×N pieces of reception signals is observed, each of the M×N pieces ofreception signals corresponding to each of the M×N pieces of complextransfer functions, extracts, among the M×N pieces of complex transferfunctions, a plurality of pairs of two complex transfer functionscorresponding to two time points in a predetermined interval, calculatesa plurality of pieces of differential information representing adifference between a pair of two complex transfer functions included ineach of the plurality of pairs of the M×N pieces of complex transferfunctions, each of the plurality of pieces of differential informationbeing expressed by a matrix of M×N dimensions, and estimates a directionto a location of the moving body with respect to the positioning sensorbased on each of the plurality of pieces of differential information.

With this configuration, a location on which a moving body exists can beestimated with high accuracy in a short period of observation timecorresponding to a cycle derived from activity of the moving body.Accordingly, a location on which a moving body exists can be highlyaccurately estimated in a short period of time by using a wirelesssignal.

(8) In the aspect, among a plurality of pairs of two time points, eachpair of two time points in a predetermined interval may include a firsttime and a second time, the first time being a point in time that may beearlier than the second time, and the first time may vary for each ofthe plurality of pairs of two complex transfer functions.

Accordingly, an influence of instantaneous noise can be reduced bytaking an average of two or more pieces of differential information, sothat accuracy in location estimation can be further improved.

(9) In the aspect, the moving body may be a living body.

(10) In the aspect, the predetermined period may be approximately a halfcycle of at least one of a breathing cycle, a heartbeat, and a bodymovement of the living body.

Accordingly, an average of two or more pieces of differentialinformation can be taken, so that accuracy in location estimation can befurther improved by reducing an influence of instantaneous noise.

It should be noted that the present disclosure can be realized not onlyas a device but also as an integrated circuit including a processingunit provided to such device, as a method including steps using aprocessing unit constituting the device, as a program causing a computerto execute the steps, and as information, data, or a signal representingthe program. The program, the information, the data, and the signal maybe distributed via a recording medium such as a CD-ROM and acommunication medium such as the Internet.

Embodiments of the present disclosure will be detailed below withreference to the accompanying drawings. Here, each of the embodimentsdescribed below represents a preferable specific example of the presentdisclosure. It should be noted that numerals, shapes, materials,components, arrangement positions and connection configurations of thecomponents, steps, an order of the steps, and the like described in thefollowing embodiments are examples and these do not limit the presentdisclosure. Further, it should be noted that components which are notdescribed in an independent claim representing the highest concept ofthe present disclosure are described as arbitrary componentsconstituting more preferable embodiments, among components in thefollowing embodiments. In the present specification and drawings,components substantively having identical functional configurations willbe denoted with identical reference characters and duplicate descriptionthereof will be omitted.

First Embodiment

A description will be provided below in which an estimation device 10according to the first embodiment estimates a direction of a moving body(living body) which is a detection object by using differentialinformation of complex transfer functions obtained by observation at twodifferent time points in a predetermined period, with reference to theaccompanying drawings.

[Configuration of Estimation Device 10]

FIG. 1 is a block diagram illustrating an example of the configurationof the estimation device 10 according to the first embodiment. FIG. 2illustrates an example of a detection object of the estimation device 10illustrated in FIG. 1.

The estimation device 10 illustrated in FIG. 1 includes an antenna unit11, a transmitter 12, a reception unit 13, a complex transfer functioncalculation unit 14, a differential information calculation unit 15, anda direction estimation processing unit 16, and estimates a direction inwhich a moving body exists.

[Transmitter 12]

The transmitter 12 generates a high frequency signal used for estimationof a direction of a living body 50. As illustrated in FIG. 2, thetransmitter 12 transmits a generated signal (transmission wave) from onepiece of transmission antenna element included in the antenna unit 11,for example.

[Antenna Unit 11]

The antenna unit 11 is composed of one piece of transmission antennaelement and N pieces (N is a natural number which is 2 or larger) ofreception antenna elements. In the present embodiment, the antenna unit11 is composed of a transmission antenna unit 11A and a receptionantenna unit 11B and the transmission antenna unit 11A includes atransmission antenna element which is a transmission antenna of oneelement and M_(R) pieces of reception antenna elements (reception arrayantenna).

As mentioned above, one piece of transmission antenna element transmitsa signal (transmission wave) generated by the transmitter 12. Then, eachof M_(R) pieces of reception antenna elements receives a signal(reception signal) which is transmitted from the one piece oftransmission antenna element and reflected by the living body 50, asillustrated in FIG. 2, for example.

[Reception Unit 13]

The reception unit 13 observes reception signals which are respectivelyreceived by N pieces of reception antenna elements and contain areflection signal, which is transmitted from the transmission antennaelement and reflected by a moving body, in the first periodcorresponding to a cycle derived from activity of the moving body. Here,the moving body is the living body 50 illustrated in FIG. 2. Further,the cycle derived from activity of the moving body is a cycle which isderived from a living body, that is, a cycle of at least one ofbreathing, heartbeat, and body movement of the living body 50(biological variation cycle).

In the present embodiment, the reception unit 13 is composed of N pieces(M_(R) pieces) of receivers (receivers 13-1 to 13-N). Each of thereceivers 13-1 to 13-N converts a high frequency signal received by acorresponding reception antenna element into a low frequency signalwhich can be subjected to signal processing. The reception unit 13transfers the low frequency signal obtained through the conversion byeach of the receivers 13-1 to 13-N to the complex transfer functioncalculation unit 14, in at least the first period.

[Complex Transfer Function Calculation Unit 14]

The complex transfer function calculation unit 14 calculates a pluralityof complex transfer functions each representing a propagationcharacteristics between the transmission antenna element and each of Npieces of reception antenna elements based on a plurality of receptionsignals observed in the first period.

In the present embodiment, the complex transfer function calculationunit 14 calculates complex transfer functions each representing apropagation characteristics between one piece of transmission antennaelement and each of M_(R) pieces of reception antenna elements based onlow frequency signals transferred from the reception unit 13. Morespecific description will be provided below with reference to FIG. 3.

FIG. 3 conceptually illustrates a state of transfer of signal waves inthe antenna unit 11 illustrated in FIG. 1. As illustrated in FIG. 3, atransmission wave transmitted from the transmission antenna element ofthe transmission antenna unit 11A is reflected by the living body 50 toreach the reception array antenna of the reception antenna unit 11B.Here, the reception array antenna is composed of M_(R) pieces ofreception antenna elements and has a linear array with an elementinterval d. Further, a direction of the living body 50 viewed from thefront of the reception array antenna is denoted as θ. It is assumed thata distance between the living body 50 and the reception array antenna issufficiently large and a reflection wave which is derived from theliving body and arrives at the reception array antenna can be consideredas a plane wave.

In this case, the complex transfer function calculation unit 14 iscapable of calculating complex transfer function vectors eachrepresenting a propagation characteristics between the transmissionantenna element and the reception array antenna, from complex receptionsignal vectors x=[x₁, . . . , x_(M) _(R) ]observed by using thereception array antenna. The complex transfer function vector can becalculated by h₀=x/s, for example. Here, s denotes a complextransmission signal and is assumed to be known.

[Differential Information Calculation Unit 15]

The differential information calculation unit 15 sequentially records aplurality of calculated complex transfer functions in time seriescorresponding to an order in which a plurality of reception signals areobserved. Then, the differential information calculation unit 15calculates two or more pieces of differential information each of whichrepresents difference between two complex transfer functionscorresponding to two time points in a predetermined interval and isexpressed by vectors of the N dimensions, among the plurality of complextransfer functions. Here, a start point between two time points in apredetermined interval is time varying between/among two or more piecesof differential information. Further, the predetermined interval may beapproximately a half of a cycle derived from the living body 50(biological variation cycle).

FIG. 4 is a conceptual diagram illustrating an example of two timepoints in a predetermined interval used in calculation of differentialinformation in the first embodiment. FIG. 5 is a conceptual diagramillustrating an example of two time points in a predetermined intervaldifferent from that in FIG. 4. In FIG. 4, the vertical axis represents avariation channel value and the horizontal axis represents time.Further, T_(meas) denotes observation time of reception signals. Thisobservation time T_(meas) is the above-mentioned first period. Theobservation time T_(meas) corresponds to the biological variationmaximum cycle of at least one of breathing, heartbeat, and body movementof a living body, for example, that is, the maximum cycle derived frombiological variation. In the example illustrated in FIG. 4, observationtime is set to approximately three seconds corresponding to a cycle ofbreathing activity of the living body 50.

In a case where a plurality of complex transfer functions calculatedfrom reception signals observed by the reception unit 13 in theobservation time T_(meas) illustrated in FIG. 4, that is, temporalvariation channels are recorded in sequence, the observation timeT_(meas) corresponds to the biological variation maximum cycle, so thatthe maximum value and the minimum value of variation of the living body50 are always included in the observation time T_(meas). Here, when thebiological variation maximum cycle is denoted as T_(max) and the minimumcycle derived from biological variation (biological variation minimumcycle) is denoted as T_(min), time subtraction of T_(max)/2 andT_(min)/2, which are half cycles of T_(max) and T_(min), is timedifference corresponding to variation of the living body 50. Therefore,a predetermined interval T in calculation of differential information ofcomplex transfer functions can be set in a range ofT_(max)/2≦T≦T_(min)/2. Thus, even when the predetermined interval T isset to be approximately a half of a cycle derived from the living body50 (biological variation cycle), components derived from the living bodycan be extracted from temporal variation channels for one cycle of theliving body 50.

Further, in the example illustrated in FIG. 4, the differentialinformation calculation unit 15 calculates differential informationrepresenting difference between complex transfer functions correspondingto different time: time t and time t+T, for example, that is, two timepoints in the predetermined interval T. Then, the differentialinformation calculation unit 15 performs the calculation of differentialinformation a plurality of times in respective predetermined intervals Twhose start points are shifted by Δt one by one. That is, thedifferential information calculation unit 15 performs such calculationof differential information in a predetermined interval T between othertwo time points (with respect to a different pair of complex transferfunctions). Here, differential information is calculated because complextransfer function components going through fixed objects other than theliving body 50 are eliminated and only complex transfer functioncomponents going only through the living body 50 are left.

In the present embodiment, there are a plurality of reception antennaelements (M_(R) pieces), so that there are a plurality of differentialvalues (pieces of differential information) of complex transferfunctions corresponding to the reception antenna unit 11B. Thesedifferential values are collectively defined as a complex differentialchannel vector. When the number of reception antenna elements is denotedas M_(R), the complex differential channel vector is expressed ash(l,m)=[h₁(l,m), . . . , h_(M) _(R) (l,m)]^(T) in which 1≦l and m≦N hold(l≠m, N denotes the total number of times of measurement). Further, land m are positive integers denoting measurement numbers and are sampletime. [•]^(T) denotes transposition. In the example illustrated in FIG.4, N denotes the number of times of channel observation and correspondsto the number of vertices of trapezoids (data used in calculation)including two time points in the time interval T such as C_(t) andC_(t+T). When the observation time T_(meas) is three seconds andmeasurement (observation) is performed 100 times, N=300 is obtained.

A complex transfer function vector calculated by the complex transferfunction calculation unit 14 contains a direct wave and reflection waveswhich do not go through the living body 50 such as a reflection wavederived from a fixed object, as illustrated in FIG. 3, for example. Onthe other hand, in a complex differential channel vector, reflectionwaves which do not go through the living body 50 are all eliminated bycalculation of difference between complex transfer function vectorscorresponding to two time points and only reflection waves derived fromthe living body are contained. This differential calculation has suchdemerit that complex transfer functions of reflection waves derived fromthe living body 50 are subtracted as well. However, since amplificationand phases of reflection waves going through the living body 50 vary atall times due to biological activity such as breathing and heartbeat, acomplex differential channel vector does not become completely 0. Thatis, when subtraction is performed between complex transfer functionvectors corresponding to two different time points, a product obtainedby multiplying a complex transfer function vector going through theliving body 50 by a coefficient is left.

Here, the differential information calculation unit 15 performscalculation of differential information with respect to a plurality ofpairs (complex transfer functions on two different time points) so as toreduce an influence of instantaneous noise and improve accuracy indirection estimation by taking an average of a plurality of times ofcalculation, as described later. In addition, the predetermined intervalT in calculation of differential information does not have to have afixed value as illustrated in FIG. 4, but may be an arbitrarypredetermined interval, that is, a predetermined interval T′ between twotime points such as time t′ and time t′+T′, illustrated in FIG. 15, forexample.

[Direction Estimation Processing Unit 16]

The direction estimation processing unit 16 estimates a direction inwhich a moving body exists based on the two or more pieces of calculateddifferential information by using the estimation device 10 as areference of a direction. More specifically, the direction estimationprocessing unit 16 calculates an instantaneous correlation matrix, whichis a correlation matrix of differential time between two time points ina predetermined interval in corresponding differential information, fromeach of the two or more pieces of calculated differential information,so as to estimate an incoming direction of a reflection signal by usingthe calculated instantaneous correlation matrix based on a predeterminedincoming direction estimation method. Then, the direction estimationprocessing unit 16 estimates a direction in which a moving body existsbased on the estimated incoming direction of a reflection signal. Here,the predetermined incoming direction estimation method is an estimationmethod based on the multiple signal classification (MUSIC) algorithm.

In the present embodiment, the direction estimation processing unit 16calculates a correlation matrix (referred to below as an “instantaneouscorrelation matrix”) shown as (Formula 1) based on complex differentialchannel vectors calculated as a plurality of pieces of differentialinformation by the differential information calculation unit 15. Thedifferential time between two time points in a predetermined interval isan instant, so that the correlation matrix shown as (Formula 1) isreferred to as the “instantaneous correlation matrix”.

R _(V)(l,m)=h(l,m)h ^(H)(l,m)  (Formula 1),

where [•]^(H) denotes complex conjugate transposition.

Further, the direction estimation processing unit 16 may average(average calculation) this instantaneous correlation matrix as expressedas (Formula 2). This is because an influence of instantaneous noise canbe reduced to improve accuracy in direction estimation due to thisaverage calculation, as described above.

$\begin{matrix}{R = {\frac{1}{N\left( {N - 1} \right)}{\sum\limits_{l = 1}^{N}\; {\sum\limits_{m = {1{({m \neq l})}}}^{N}\; {R_{i}\left( {l,m} \right)}}}}} & \left( {{Formula}\mspace{14mu} 2} \right)\end{matrix}$

Here, the rank of the instantaneous correlation matrix expressed as(Formula 1) is 1. This instantaneous correlation matrix is obtained byconverting a vector of 4×1 into a matrix of 4×4 and is merely a matrixincluding rows obtained by multiplying a single row component by aninteger. Therefore, a simultaneous equation cannot be solved, that is,this instantaneous correlation matrix is on the rank 1.

However, the rank of the correlation matrix can be restored by averagecalculation of the instantaneous correlation matrix. That is, as aneigenvalue (rank) can be increased by averaging (Formula 1) as (Formula2), variables (targets) to be able to be solved can be increased.Accordingly, eigenvalues of (Formula 2) are increased, and thus,estimation accuracy can be improved. Further, though described later,simultaneous estimation of a plurality of incoming waves is enabled.Here, average calculation is often used to improve accuracy in thelater-described MUSIC method and is usually performed by using afrequency component. On the other hand, the present embodiment isdifferent in that averaging is performed in a temporal direction.

Thus, complex transfer functions are recorded in a time series manner ina certain period and (all of) the recorded complex transfer functionsare used, providing such advantageous effect that estimation accuracycan be improved even in a case where an observation period is relativelyshort (for example, a few seconds).

The direction estimation processing unit 16 is capable of estimating anincoming direction of a reflection signal by using the instantaneouscorrelation matrix calculated as described above.

A method for performing direction estimation by using an instantaneouscorrelation matrix obtained from complex differential channel vectorswill be described below. An estimation method based on the MUSICalgorithm will be described here.

When the instantaneous correlation matrix expressed as (Formula 2) issubjected to eigenvalue decomposition, R=∪Λ∪^(H), ∪=[u₁, . . . , u_(L),u_(L+1), . . . , u_(M) _(R) ], and Λ=diag[λ₁, . . . , λ_(L), λ_(L+1), .. . , λ_(M) _(R) ] are obtained.

Here, u₁, . . . , u_(M) _(R) are eigenvectors whose number of elementsis M_(R) and λ₁, . . . , λ_(M) _(R) are eigenvalues corresponding toeigenvectors and are in an order of λ₁≧λ₂≧ . . . λ_(L), λ_(L+1)≧λ_(M)_(R) . L denotes the number of incoming waves, that is, the number ofliving bodies which are detection objects.

Further, a steering vector (direction vector) of the reception arrayantenna is defined as a(θ)=[1, e^(−jkd sin θ,) . . . , e^(−jkd(M) ^(R)^(−1)sin θ,)]^(T) and the MUSIC method is applied to a(θ)=[1,e^(−jkd sin θ,) . . . , e^(−jkd(M) ^(R) ^(−1)sin θ,)]^(T). Here, kdenotes the number of waves.

That is, the direction estimation processing unit 16 searches the localmaximal value of an evaluation function P_(music)(θ) expressed below byusing the steering vector of the reception array antenna based on theMUSIC method, being able to estimate the direction of an incoming wave.

${P_{music}(\theta)} = \frac{1}{{{{a^{H}(\theta)}\left\lbrack {u_{L + 1},\ldots \mspace{11mu},u_{M_{R}}} \right\rbrack}}^{2}}$

The direction estimation processing unit 16 thus performs eigenvaluedecomposition of the instantaneous correlation matrix and applies theMUSIC method so as to be able to estimate an incoming direction of areflection signal. Accordingly, the direction estimation processing unit16 can estimate a direction in which the living body 50 exists based onthe estimated incoming direction of the reflection signal. This isbecause the estimated incoming direction of the reflection signal isapproximately accorded with the direction in which the living body 50exists relative to the estimation device 10.

[Operation of Estimation Device 10]

An operation in estimation processing of the estimation device 10configured as described above will be described. FIG. 6 is a flowchartillustrating the estimation processing of the estimation device 10according to the first embodiment.

The estimation device 10 first observes reception signals containing areflection signal, which is transmitted from one piece of transmissionantenna element and reflected by the living body 50, in the first periodcorresponding to a cycle derived from activity of the living body 50(S10).

Then, the estimation device 10 calculates a plurality of complextransfer functions each representing a propagation characteristicsbetween one piece of transmission antenna element and each of M_(R)pieces of reception antenna elements based on a plurality of receptionsignals observed in the first period (S20). As the detailed descriptionhas been provided above, provision of the description is omitted here.The same goes for the following.

Subsequently, the estimation device 10 calculates two or more pieces ofdifferential information each representing difference between twocomplex transfer functions corresponding to two time points in apredetermined interval, among a plurality of complex transfer functions(S30).

Then, the estimation device 10 estimates a direction in which the livingbody 50 exists by using the two or more pieces of differentialinformation (S40).

[Advantageous Effects Etc.]

According to the estimation device 10 and the estimation method of thepresent embodiment, by calculating the above-described differentialinformation, signal processing by which only components derived from aliving body are left in a wireless signal can be performed without usingthe Fourier transformation in shorter processing time than that in thecase using the Fourier transformation. Further, estimation accuracy canbe improved by using a plurality of pieces of differential information.Accordingly, a direction in which a moving body exists can be highlyaccurately estimated in short observation time corresponding to a cyclederived from activity of the moving body. Thus, a direction in which amoving body exists can be highly accurately estimated in a short periodof time by using a wireless signal.

Embodiment 2

In the first embodiment, the description of the estimation device 10 andthe estimation method thereof is provided in which a direction in whicha moving body (living body), which is a detection object, exists isestimated by using differential information of complex transferfunctions observed at two different time points in a predeterminedperiod. In the second embodiment, an estimation device 20 and anestimation method thereof will be described in which a location of amoving body (living body), which is a detection object, is estimated byusing similar differential information.

[Configuration of Estimation Device 20]

FIG. 7 is a block diagram illustrating an example of the configurationof the estimation device 20 according to the second embodiment. FIG. 8illustrates an example of a detection object of the estimation device 20illustrated in FIG. 7. Components similar to those in FIG. 1 and FIG. 2are denoted with the same reference characters and detailed descriptionthereof will be omitted.

The estimation device 20 illustrated in FIG. 7 includes a transmissionantenna unit 21A, a reception antenna unit 21B, a transmission unit 22,a reception unit 23, a complex transfer function calculation unit 24, adifferential information calculation unit 25, and a location estimationprocessing unit 26, and estimates a location of a moving body. At leastthe number of transmission antenna elements of the estimation device 20illustrated in FIG. 7 is different from that in the estimation device 10illustrated in FIG. 1 and accordingly, the estimation device 20 iscapable of estimating a location of a moving body.

[Transmission Unit 22]

The transmission unit 22 generates a high frequency signal used forestimation of a direction of the living body 50. As illustrated in FIG.8, the transmission unit 22 transmits a generated signal (transmissionwave) from M_(T) pieces of transmission antenna elements (transmissionarray antenna) included in the transmission antenna unit 21A, forexample.

[Transmission Antenna Unit 21A]

The transmission antenna unit 21A is composed of M pieces (M is anatural number which is 2 or larger) of transmission antenna elements.In the present embodiment, the transmission antenna unit 21A includesM_(T) pieces of transmission antenna elements. As mentioned above, theM_(T) pieces of transmission antenna elements transmit signals(transmission waves) generated by the transmission unit 22.

[Reception Antenna Unit 21B]

The reception antenna unit 21B is composed of N pieces (N is a naturalnumber which is 2 or larger) of reception antenna elements (receptionarray antenna). As is the case with the first embodiment, the receptionantenna unit 21B includes M_(R) pieces of reception antenna elements(reception array antenna) in the present embodiment. As illustrated inFIG. 8, for example, each of the M_(R) pieces of reception antennaelements receives a signal (reception signal) which is transmitted fromthe M_(T) pieces of transmission antenna elements (transmission arrayantenna) and reflected by the living body 50.

[Reception Unit 23]

The reception unit 23 observes reception signals which are respectivelyreceived by the N pieces of reception antenna elements and containreflection signals, which are respectively transmitted from the M piecesof transmission antenna elements and reflected by a moving body, in thefirst period corresponding to a cycle derived from activity of themoving body. Here, the moving body is the living body 50 illustrated inFIG. 8. Further, the cycle derived from activity of the moving body is acycle which is derived from a living body, that is, a cycle of at leastone of breathing, heartbeat, and body movement of the living body 50(biological variation cycle).

In the present embodiment, the reception unit 23 is composed of M_(R)pieces of receivers. Each of the M_(R) pieces of receivers converts ahigh frequency signal received by a corresponding reception antennaelement into a low frequency signal which can be subjected to signalprocessing. The reception unit 23 transfers the low frequency signalobtained through the conversion by each of the M_(R) pieces of receiversto the complex transfer function calculation unit 24, in at least thefirst period.

[Complex Transfer Function Calculation Unit 24]

The complex transfer function calculation unit 24 calculates a pluralityof complex transfer functions each representing a propagationcharacteristics between each of the M pieces of transmission antennaelements and each of the N pieces of reception antenna elements based ona plurality of reception signals observed in the first period.

In the present embodiment, the complex transfer function calculationunit 24 calculates complex transfer functions representing propagationproperties between the M_(T) pieces of transmission antenna elements andthe M_(R) pieces of reception antenna elements based on low frequencysignals transferred from the reception unit 13. More specificdescription will be provided with reference to FIG. 8.

In FIG. 8, both of the transmission array antenna and the receptionarray antenna have a linear array with element interval d and directionsof the living body 50 viewed from the front of the transmission arrayantenna and the front of the reception array antenna are respectivelydenoted as θ_(T) and O_(R). It is assumed that a distance between theliving body and the transmission/reception array antenna is sufficientlylarge compared to an opening width of the array antenna and a signalwhich starts from the transmission array antenna and goes through theliving body to reach the reception array antenna can be considered as aplane wave.

As illustrated in FIG. 8, transmission waves transmitted from the M_(T)pieces of transmission antenna elements (transmission array antenna) ofthe transmission antenna unit 21A by the angle θ_(T) are reflected bythe living body 50 to reach the reception array antenna by the angleθ_(R).

In this case, the complex transfer function calculation unit 24 iscapable of calculating complex transfer function vectors from complexreception signal vectors observed by using the reception array antenna.The complex transfer function vectors are expressed in a matrix form andcan be calculated in a similar manner to the first embodiment. Here, thecalculated complex transfer function matrix contains a direct wave andreflection waves which do not go through the living body 50 such as areflection wave derived from a fixed object, as described above.

[Differential Information Calculation Unit 25]

The differential information calculation unit 25 sequentially records aplurality of calculated complex transfer functions in time seriescorresponding to an order in which a plurality of reception signals areobserved. Then, the differential information calculation unit 25calculates two or more pieces of differential information each of whichrepresents difference between two complex transfer functionscorresponding to two time points in a predetermined interval and isexpressed by a matrix of the M×N dimensions, among the plurality ofcomplex transfer functions. Here, a start point between two time pointsin a predetermined interval is time varying between/among two or morepieces of differential information. Further, the predetermined intervalmay be approximately a half of a cycle derived from the living body 50(biological variation cycle).

Here, as two time points in a predetermined interval used in calculationof differential information have been described in the first embodimentwith reference to FIG. 4, the description thereof is omitted here.

In the present embodiment as well, the differential informationcalculation unit 25 calculates differential information representingdifference between two complex transfer functions corresponding to twodifferent time points in the predetermined interval T among complextransfer functions calculated by the complex transfer functioncalculation unit 24. Further, the differential information calculationunit 25 executes calculation of differential information with respect toother two different time points (a different pair of complex transferfunctions) as well. Here, differential information is calculated so asto eliminate complex transfer function components going through fixedobjects other than the living body 50 and leave only complex transferfunction components going only through the living body 50, as is thecase with the first embodiment.

In the present embodiment, a plurality of transmission antenna elementsand a plurality of reception antenna elements are provided. Therefore,the number of differential values (pieces of differential information)of complex transfer functions corresponding to the transmission antennaunit 21A and the reception antenna unit 21B is the number obtained bytransmission antenna elements×reception antenna elements (M_(R)×M_(T))and these differential values are collectively defined as a complexdifferential channel matrix H(l,m). The differential informationcalculation unit 25 calculates, as the differential information, acomplex differential channel matrix H(l,m) expressed as the following.In this complex differential channel matrix H(l,m), reflection waveswhich do not go through the living body 50 are all eliminated bydifferential calculation and therefore, only reflection waves derivedfrom the living body 50 are contained.

${H\left( {l,m} \right)} = \begin{bmatrix}{h_{11}\left( {l,m} \right)} & \cdots & {h_{1M_{T}}\left( {l,m} \right)} \\\vdots & \ddots & \vdots \\{h_{M_{R}1}\left( {l,m} \right)} & \cdots & {h_{1M_{R}M_{T}}\left( {l,m} \right)}\end{bmatrix}$

Here, 1≦l and m≦N hold (l≠m, N denotes the total number of times ofmeasurement). Further, l and m are positive integers denotingmeasurement numbers and are sample time.

[Location Estimation Processing Unit 26]

The location estimation processing unit 26 estimates a location on whicha moving body exists based on the two or more pieces of calculateddifferential information. More specifically, the location estimationprocessing unit 26 calculates an instantaneous correlation matrix, whichis a correlation matrix of differential time between two time points ina predetermined interval in corresponding differential information, fromeach of the two or more pieces of calculated differential information.Then, the location estimation processing unit 26 estimates atransmission direction of a transmission signal which is transmittedfrom the transmission antenna unit 21A to the moving body and anincoming direction of a reflection signal by using the calculatedinstantaneous correlation matrix based on a predetermined incomingdirection estimation method. Subsequently, the location estimationprocessing unit 26 estimates the location on which the moving bodyexists, based on the estimated transmission direction of a transmissionsignal and the estimated incoming direction of the reflection signal.Here, the predetermined incoming direction estimation method is anestimation method based on the MUSIC algorithm.

In the present embodiment, the location estimation processing unit 26calculates an instantaneous correlation matrix based on a complexdifferential channel matrix calculated as a plurality of pieces ofdifferential information by the differential information calculationunit 25.

More specifically, the location estimation processing unit 26 rearrangeselements of the above-mentioned complex differential channel matrixH(l,m) calculated by the differential information calculation unit 25 soas to calculate a complex differential channel of a vector ofM_(R)M_(T)×1 expressed as (Formula 3).

$\begin{matrix}\begin{matrix}{{h_{v}\left( {l,m} \right)} = {{vec}\left( {H\left( {l,m} \right)} \right)}} \\{= \left\lbrack {{h_{11}\left( {l,m} \right)},\cdots \mspace{11mu},{h_{M_{R}1}\left( {l,m} \right)},{h_{12}\left( {l,m} \right)},\cdots \mspace{11mu},} \right.} \\\left. {{h_{M_{R}2}\left( {l,m} \right)},\cdots \mspace{11mu},{h_{M_{R}M_{T}}\left( {l,m} \right)}} \right\rbrack^{T}\end{matrix} & \left( {{Formula}\mspace{14mu} 3} \right)\end{matrix}$

Here, vec(•) represents conversion of a matrix into a vector.

Then, the location estimation processing unit 26 calculates aninstantaneous correlation matrix expressed as (Formula 4) from thiscomplex differential channel vector.

R _(i)(l,m)=h _(V)(l,m)h _(V) ^(H)(l,m)  (Formula 4)

Further, the location estimation processing unit 26 may average (averagecalculation) this instantaneous correlation matrix as expressed as(Formula 5). This is because an influence of instantaneous noise can bereduced to improve accuracy in direction estimation due to this averagecalculation, as described above.

$\begin{matrix}{R = {\frac{1}{N\left( {N - 1} \right)}{\sum\limits_{l = 1}^{N}\; {\sum\limits_{m = {1{({m \neq l})}}}^{N}\; {R_{i}\left( {l,m} \right)}}}}} & \left( {{Formula}\mspace{14mu} 5} \right)\end{matrix}$

Here, the rank of the instantaneous correlation matrix of (Formula 4) is1, but the rank of the correlation matrix can be restored by averagecalculation of the instantaneous correlation matrix, as described in thefirst embodiment as well. This enables not only improvement inestimation accuracy but also simultaneous estimation of a plurality ofincoming waves.

Thus, complex transfer functions are recorded in a time series manner ina certain period and (all of) the recorded complex transfer functionsare used, providing such advantageous effect that estimation accuracycan be improved even in a case where an observation period is relativelyshort (for example, a few seconds).

The location estimation processing unit 26 is capable of estimating alocation of the living body 50 by using the instantaneous correlationmatrix calculated as described above.

A method for performing direction estimation by using an instantaneouscorrelation matrix obtained from a complex differential channel matrixwill now be described. An estimation method based on the MUSIC algorithmwill be described in the present embodiment as well.

When the instantaneous correlation matrix expressed as (Formula 5) issubjected to eigenvalue decomposition, R=∪Λ∪^(H), ∪=[u₁, . . . , u_(L),u_(L+1), . . . , u_(M) _(R) ], and Λ=diag[λ₁, . . . , λ_(L), λ_(L+1), .. . , λ_(M) _(R) ] are obtained.

Here, u₁, . . . , u_(M) _(R) are eigenvectors whose number of elementsis M_(R) and λ₁, . . . , λ_(M) _(R) are eigenvalues corresponding toeigenvectors and are in an order of λ₁≧λ₂≧ . . . ≧λ_(L), λ_(L+1)≧ . . .≧λ_(M) _(R) . L denotes the number of incoming waves, that is, thenumber of living bodies which are detection objects.

Further, a steering vector (direction vector) of the transmission arrayantenna is defined as a_(T)(θ_(T))=[1,e^(−jkd sin θ) ^(T) ^(,) . . . ,e^(−jkd(M) ^(T) ^(−1)sin θ) ^(T) ]^(T) and a steering vector of thereception array antenna (direction vector) is defined asa_(R)(θ_(R))=[1,e^(−jkd sin θ) ^(R) ^(,) . . . , e^(−jkd(M) ^(R)^(−1)sin θ) ^(R) ]^(T). Here, k denotes the number of waves.

Further, these steering vectors are subjected to multiplication so as todefine a steering vector considering angle information of both of thetransmission array antenna and the reception array antenna asa(θ_(T),θ_(R))=vec{a_(r)(θ_(r))a_(R) ^(T)(,θ_(R))} and the MUSIC methodis applied to the steering vector.

That is, the location estimation processing unit 26 searches the localmaximal value with an evaluation function P_(music)(θ) expressed belowby using the multiplied steering vector based on the MUSIC method, beingable to estimate the direction of an incoming wave.

${P_{music}(\theta)} = \frac{1}{{{{a^{H}\left( {\theta_{T},\theta_{R}} \right)}\left\lbrack {u_{L + 1},\ldots \mspace{11mu},u_{M_{R}}} \right\rbrack}}^{2}}$

In the present embodiment, since it is necessary to search the localmaximal value of an evaluation function for two angles (θ_(T),θ_(R)),two-dimensional search processing is executed. Then, the locationestimation processing unit 26 estimates a transmission direction of atransmission wave to the living body 50 and an incoming direction of areflection wave from the living body 50 based on the two angles(θ_(T),θ_(R)) thus obtained so as to estimate a location of the livingbody 50 based on an intersection of the estimated two directions.

[Operation of Estimation Device 20]

An operation in estimation processing of the estimation device 20configured as described above will be described. FIG. 9 is a flowchartillustrating the estimation processing of the estimation device 20according to the second embodiment.

The estimation device 20 first observes reception signals containing areflection signal which is transmitted from M_(T) pieces of transmissionantenna elements and reflected by the living body 50, in the firstperiod corresponding to a cycle derived from activity of the living body50 (S10A).

Then, the estimation device 20 calculates a plurality of complextransfer functions each representing a propagation characteristicsbetween each of the M_(T) pieces of transmission antenna elements andeach of the M_(R) pieces of reception antenna elements based on aplurality of reception signals observed in the first period (S20A). Asthe detailed description has been provided above, provision of thedescription is omitted here. The same goes for the following.

Subsequently, the estimation device 20 calculates two or more pieces ofdifferential information each representing difference between twocomplex transfer functions corresponding to two time points in apredetermined interval among a plurality of complex transfer functions(S30A).

Then, the estimation device 20 estimates a location on which the livingbody 50 exists by using the two or more pieces of differentialinformation (S40A).

[Advantageous Effects Etc.]

According to the estimation device 20 and the estimation method of thepresent embodiment, by calculating the above-described differentialinformation, signal processing by which only components derived from aliving body are left in a wireless signal can be performed without usingthe Fourier transformation in shorter processing time than that in thecase using the Fourier transformation. Further, estimation accuracy canbe improved by using a plurality of pieces of differential information.Accordingly, a direction in which a moving body exists can be highlyaccurately estimated in short observation time corresponding to a cyclederived from activity of the moving body. Thus, a location on which amoving body exists can be highly accurately estimated in a short periodof time by using a wireless signal.

Here, evaluation was performed by an experiment so as to confirm theadvantageous effect according to the second embodiment. The evaluationwill be described below.

FIG. 10 illustrates a concept of an experiment using the estimationmethod according to the second embodiment.

Both of the transmission array antenna (Tx array) and the receptionarray antenna (Rx array) which are illustrated in FIG. 10 have the 4×4multiple input multiple output (MIMO) configuration using a four-elementpatch array antenna. Further, the single-pole-4-throw (SP4T) switch anda receiver of four systems were disposed respectively on thetransmission side and the reception side. In this experiment,measurement of a MIMO channel was performed by using these devices.

Here, the array element interval of the transmission/reception antennaswas set to 0.5 wavelength, the transmission-reception distance D was setto 4.0 m, and the antenna height h was set to 1.0 m which is the heightof a chest of an erecting human being (living body). A non-modulatedcontinuous wave (CW) of 2.47125 GHz was transmitted from a transmitter,the sampling frequency (channel acquisition speed) was set to 7.0 Hz,and channel measurement time was set to 3.3 seconds. In the channelmeasurement, there was no person other than a subject and the subjectfaced the front to a wall on the antenna side.

FIG. 11 illustrates a result of an experiment using the estimationmethod according to the second embodiment. FIG. 11 illustrates a resultof living body location estimation in a case of two subjects. Thestanding location of the subject 1 was (X=1.0 m, Y=2.5 m) and thestanding location of the subject 2 was (X=3.0 m, Y=2.0 m). In FIG. 11,the actual locations of the subjects are marked with a circle andlocations of the subjects estimated through search of the local maximalvalue of an evaluation function are marked with a diamond. Asillustrated in FIG. 11, the locations of the subjects estimated throughthe search of the local maximal values of evaluation functions are closeto the actual subjects (living bodies) in the case of two subjects aswell. Accordingly, it is understood that the estimation method accordingto the second embodiment enables estimation of living body locations ofa plurality of persons.

FIG. 12 illustrates a result of another experiment using the estimationmethod according to the second embodiment. A solid line A of FIG. 12indicates a cumulative probability distribution (a cumulativedistribution function (CDF)) of location estimation errors when livingbody location estimation in the case of two subjects was tested 1500times. The dotted line B of FIG. 12 is shown together as a comparisonexample which is a result (a cumulative probability distribution oflocation estimation errors) of the living body location estimationmethod (the above-mentioned Japanese Unexamined Patent ApplicationPublication No. 2015-117972) using the Fourier transformation which isthe method of a related art with respect to a time-variation channel on3.28 seconds which is a condition of this experiment.

Referring to FIG. 12, the CDF 90% value in the case of the comparisonexample using the Fourier transformation is 1.12 m and the CDF 90% valuein the case of the use of the estimation method according to the secondembodiment is 0.39 m. Accordingly, it is understood that the estimationobtained by the estimation method according to the second embodiment ismore accurate by 0.73 m. Thus, it is verified that a living bodylocation can be estimated with high accuracy even in short observationtime due to the present embodiment.

As described above, according to the present disclosure, by calculatingdifferential information which is difference between propagationchannels at two different time points in a predetermined period, signalprocessing by which only components derived from a living body are leftin a wireless signal can be performed without using the Fouriertransformation in shorter processing time than that in the case usingthe Fourier transformation. Further, estimation accuracy can be improvedby using a plurality of pieces of differential information. Accordingly,a direction in which a moving body exists can be estimated with highaccuracy in short observation time corresponding to a cycle derived fromactivity of the moving body. Thus, an estimation device and anestimation method can be realized by which a direction and a location onwhich a moving body exists can be highly accurately estimated in a shortperiod of time by using a wireless signal.

The positioning sensor and the direction estimation method according toone aspect of the present disclosure have been described above based onthe embodiments, but the present disclosure is not limited to theseembodiments. An aspect in which various modifications conceived by aperson skilled in the art are applied to the above embodiments and anaspect constructed by combining components of the different embodimentsare also included in the scope of the present disclosure as long as theaspects do not deviate from the purpose of the present disclosure.

For example, in the first and second embodiments, the directionestimation and the location estimation of the living body 50 have beendescribed as examples, but an object is not limited to the living body50. The present disclosure is applicable to various moving bodies (amachine and so forth) which provide the Doppler effect to a reflectionwave by activity thereof in a case where the moving bodies areirradiated with a high frequency signal.

Further, the present disclosure can be realized not only as apositioning sensor which is provided with such discriminative componentsbut also as an estimation method including steps of discriminativecomponents included in the positioning sensor. Further, the presentdisclosure can be realized also as a computer program causing a computerto execute each discriminative step included in such method. It isneedless to say that such computer program can be distributed via anon-temporal recording medium which is readable by a computer such as aCD-ROM or via a communication network such as the Internet.

The present disclosure is applicable to a positioning sensor and adirection estimation method for estimating a direction and a location ofa moving body by using a wireless signal, and especially applicable to apositioning sensor, which is mounted on measuring instrument whichmeasures a direction and a location of a moving body such as a livingbody and a machine, an electrical household appliance which performscontrol corresponding to a direction and a location of a moving body, amonitoring device which detects intrusion of a moving body, and the likeand a direction estimation method.

What is claimed is:
 1. A positioning sensor comprising: a transmission antenna that transmits a transmission signal to a predetermined area in search of a moving body; a plurality of reception antennae, each of which receives a reception signal, one or more of a plurality of the reception signals received includes a reflection signal generated by the moving body reflecting the transmission signal; a receiver that observes each of the plurality of reception signals in a predetermined sampling cycle in a predetermined period; a processor; and a memory, wherein the processor calculates a plurality of complex transfer functions, each of the plurality of complex transfer functions representing a propagation characteristics between the transmission antenna and each of the plurality of reception antennae based on each of the plurality of reception signals, records each of the plurality of complex transfer functions in the memory as being associated with each time point at which each of the plurality of reception signals is observed, each of the plurality of reception signals corresponding to each of the plurality of complex transfer functions, extracts, among the plurality of complex transfer functions, a plurality of pairs of two complex transfer functions respectively corresponding to two time points in a predetermined interval, calculates a plurality of pieces of differential information representing a difference between a pair of two complex transfer functions included in each of the plurality of pairs of two complex transfer functions, each of the plurality of pieces of differential information being expressed by a vector of N dimensions, and estimates a direction to a location of the moving body with respect to the positioning sensor based on each of the plurality of pieces of differential information.
 2. The positioning sensor according to claim 1, wherein among a plurality of pairs of two time points, each pair of two time points in a predetermined interval includes a first time and a second time, the first time being a point in time that is earlier than the second time, and the first time varies for each of the plurality of pairs of two complex transfer functions.
 3. The positioning sensor according to claim 1, wherein the moving body is a living body.
 4. The positioning sensor according to claim 3, wherein the predetermined period is approximately a half cycle of at least one of a breathing cycle, a heartbeat, and a body movement of the living body.
 5. The positioning sensor according to claim 1, wherein among a plurality of pairs of two time points, each pair of two time points in a predetermined interval includes a first time and a second time, the first time being a point in time that is earlier time than the second time, and the processor for each of the plurality of pairs of two time points, calculates a correlation matrix with respect to a differential time between the second time and the first time based on each of the plurality of pieces of differential information, applies a predetermined method to each of the correlation matrices to estimate an incoming direction of the reflection signal with respect to the positioning sensor, and estimates a direction to a location of the moving body with respect to the positioning sensor based on the incoming direction.
 6. The positioning sensor according to claim 5, wherein the predetermined method is a multiple signal classification algorithm.
 7. A positioning sensor comprising: M transmission antennae, M being a natural number of 2 or larger, each transmission antenna transmits a transmission signal to a predetermined area in search of a moving body; N reception antennae, N being a natural number of 2 or larger, each reception antenna receives a reception signal, one or more of the reception signals received includes a reflection signal generated by the moving body reflecting the transmission signal; a receiver that observes each of the reception signals received in a predetermined sampling cycle in a predetermined period; a processor; and a memory, wherein the processor calculates M×N pieces of complex transfer functions, each of the M×N pieces of complex transfer functions representing a propagation characteristics between each of the M transmission antennae and each of the N reception antennae based on each of the reception signals received, records each of the M×N pieces of complex transfer functions in the memory as being associated with each time point at which each of M×N pieces of reception signals is observed, each of the M×N pieces of reception signals corresponding to each of the M×N pieces of complex transfer functions, extracts, among the M×N pieces of complex transfer functions, a plurality of pairs of two complex transfer functions corresponding to two time points in a predetermined interval, calculates a plurality of pieces of differential information representing a difference between a pair of two complex transfer functions included in each of the plurality of pairs of the M×N pieces of complex transfer functions, each of the plurality of pieces of differential information being expressed by a matrix of M×N dimensions, and estimates a direction to a location of the moving body with respect to the positioning sensor based on each of the plurality of pieces of differential information.
 8. The positioning sensor according to claim 7, wherein among a plurality of pairs of two time points, each pair of two time points in a predetermined interval includes a first time and a second time, the first time being a point in time that is earlier than the second time, and the first time varies for each of the plurality of pairs of two complex transfer functions.
 9. The positioning sensor according to claim 7, wherein the moving body is a living body.
 10. The positioning sensor according to claim 9, wherein the predetermined period is approximately a half cycle of at least one of a breathing cycle, a heartbeat, and a body movement of the living body.
 11. The positioning sensor according to claim 7, wherein among a plurality of pairs of two time points, each pair of two time points in a predetermined interval includes a first time and a second time, the first time being a point in time that is earlier than the second time, and the processor for each of the plurality of pairs of two time points, calculates a correlation matrix with respect to a differential time between the second time and the first time based on each of the plurality of pieces of differential information, applies a predetermined method to each of the correlation matrices to estimate an incoming direction of the reflection signal with respect to the positioning sensor, and estimates a direction to a location of the moving body with respect to the positioning sensor based on the incoming direction.
 12. The positioning sensor according to claim 11, wherein the predetermined method is a multiple signal classification algorithm.
 13. A method for estimating an incoming direction of a signal in a positioning sensor, the positioning sensor including a transmission antenna that transmits a transmission signal to a predetermined area in search of a moving body, a plurality of reception antennae, each of which receives a reception signal, one or more a plurality of the reception signals received includes a reflection signal generated by the moving body reflecting the transmission signal, a receiver that observes each of the plurality of reception signals in a predetermined sampling cycle in a predetermined period, a processor, and a memory, the method comprising: calculating, by the processor, a plurality of complex transfer functions, each of the plurality of complex transfer functions representing a propagation characteristics between the transmission antenna and each of the plurality of reception antennae based on each of the plurality of reception signals; recording, by the processor, each of the plurality of complex transfer functions in the memory as being associated with each time point at which each of the plurality of reception signals is observed, each of the plurality of reception signals corresponding to each of the plurality of complex transfer functions; extracting, by the processor and among the plurality of complex transfer functions, a plurality of pairs of two complex transfer functions respectively corresponding to two time points in a predetermined interval; calculating, by the processor, a plurality of pieces of differential information representing a difference between a pair of two complex transfer functions included in each of the plurality of pairs of two complex transfer functions, each of the plurality of pieces of differential information being expressed by a vector of N dimensions; and estimating, by the processor, a direction to a location of the moving body, with respect to the positioning sensor based on each of the plurality of pieces of differential information.
 14. A method for estimating an incoming direction of a signal in a positioning sensor, the positioning sensor including M transmission antennae, M being a natural number of 2 or larger, each transmission antenna transmits a transmission signal to a predetermined area in search of a moving body, N reception antennae, N being a natural number of 2 or larger, each reception antenna receives a reception signal, one or more of the reception signals received includes a reflection signal generated by the moving body reflecting the transmission signal, a receiver that observes each of the reception signals received in a predetermined sampling cycle in a predetermined period, a processor, and a memory, the method comprising: calculating, by the processor, M×N pieces of complex transfer functions, each of the M×N pieces of complex transfer functions representing a propagation characteristics between each of the M transmission antennae and each of the N reception antennae based on each of the reception signals received; recording, by the processor to the memory, each of the M×N pieces of complex transfer functions as being associated with each time point at which each of M×N pieces of reception signals is observed, each of the M×N pieces of reception signals corresponding to each of the M×N pieces of complex transfer functions; extracting, by the processor and among the M×N pieces of complex transfer functions, a plurality of pairs of two complex transfer functions corresponding to two time points in a predetermined interval; calculating, by the processor, a plurality of pieces of differential information representing a difference between a pair of two complex transfer functions included in each of the plurality of pairs of the M×N pieces of complex transfer functions, each of the plurality of pieces of differential information being expressed by a matrix of M×N dimensions; and estimating, by the processor, a direction to a location of the moving body with respect to the positioning sensor based on each of the plurality of pieces of differential information. 